LBS and IoT: not just what but where

LBS, Location-Based Services, are most-widely known in association with mobile devices (e.g. smart phones) with a built-in GNSS [Global Navigation Satellite System1] receiver, picking up signals from earth satellites, or correlating and trilaterating signal strengths of known WiFi or mobile telephone networks, especially useful when inside a building where satellite signals do not penetrate.

Once an absolute location has been recorded, relative positioning can take over. Technology such as wheel rotation counters and magnetic bearings can be used to deduce location over time. In modern smart phones, MEMS sensors can detect acceleration and orientation, allowing navigation to continue in low-signal environments, such as through urban canyons.

Public

LBS were initiated by the FCC in 1996 as the E911 mandate, and in Europe in 2002 under the E112 mandate. The aim was to improve the location information of a mobile caller to an emergency number to allow for quicker and more-accurate response to the location.

Since then, based on the technology of emergency response LBS, commercial services have sprung up surrounding navigation and location: public transportation, people and asset tracking, travel and tourism, local search, social networking, fitness, mobile advertising.

Commercial

Many people have LBS activated on their mobile phones throughout the day, allowing companies like Google to push “useful” location-based information to them based on their and others’ locations. Consider being informed of a traffic conditions ahead by Google Maps that was deduced by the location and slow speed of other Google “informants” caught up in the traffic. Other applications include tailored fitness apps (MapMyWalk, MapMyRun, etc.), whether for cycling, running or walking. Or Foursquare, billed as a concierge that will suggest to you and your friends the best places to go for entertainment and dining in your (immediate) area.

LBS for IoT

But nowadays, not only mobile phones are mobile: IoT devices are also mobile and will need their own location-based services to allow them to properly interact with their environment. These devices could be sewn into our clothing and connect to a Body-Area Network (BAN) or Personal-Area Network (PAN), which eventually connects to our phones, and out to the Internet. Applications include health monitoring or fitness, but could include pay-as-you-go car (or sky-diving) insurance! In the future, we may not actually own our IoT devices, as they will be shared, like public bicycles in London, and so their location information is necessary to be able to track them and who is riding them at the time.

The geographic location information can be any type of data, so long as it involves and incorporates some form of location. To aid in series measurements, a time axis may also be incorporated into the data record.

Scattered, Drifting, and Embedded

Motes (Specs, or SmartDust) are an early form of ZigBee-connected sensor that are deployed by aerial drop on to a battelfield to detect enemy movements: pressure sensors (gunfire and movement), light sensors (shadows and lamps), sounds, etc.

Environmental buoys and water-column sensors can track spills or monitor water quality as they drift. Scattering motes at spill sites can enable the spill to be categorised as cleaned up when no motes are found at the site, as well as allow the spill to be monitored in real-time during clean-up.

Motes can also be embedded in concrete during a pour and allow the structure (road, bridge, building) to be monitored throughout its life. With the advent of contactless power supply, motes can be activated during a sweep of the structure and return their measurements.

Stationary

IoT devices may still be connected to a LBS but as a stationary node: a beacon. Apple and Google have introduced the iBeacon and Eddystone beacon protocols, respectively. Beacons simply broadcast a regular BLE (BlueTooth Low Energy) signal that can “anchor” a mobile device to a location, even for a split second, allowing location-specific information to be associated with the mobile device ephemerally.

Consider a coffee shop that pings special offers to phones running their loyalty app: as a mobile device travels past, it can advertise a special offer. By spreading beacons around a block or shopping mall it could help guide prospective patrons to the shop.

Legal and Ethical Issues

As data on our movements are collected, behaviour patterns can be analysed and also altered: we drive down routes unknown to us based on the suggestion of our GPS navigation unit; we stop at a coffee shop based on some mobile advertising; we allow our washing machine to pause when we are out of the house when there is a high load on the power network.

From a legal point of view, data generated through location-aware mobile and IoT devices is regarded as a new, distinct class of data that requires increased protection and special procedures to collect, analyse and act upon. The EU Directive on Privacy and Electronic Communications ensures that location data can only be used with the permission of the user, but what of IoT devices that simply know a mobile device has passed by its location (again, three times since last week and always at 12:30)?

Location (and time) is a context attributed to a piece of data and can be attributed a meaning. The locale could be very sensitive: what if the IoT device was located in the toilets of a club catering to a particular sexual persuasion and it noted your mobile device lingered there for more than 10 minutes; what if the IoT device detected your mobile device visiting the headquarters of a competing employer; what if the IoT device detected your mobile phone at a prison or law courts when you had called in sick?

As a consequence of the context, one can uncover knowledge from the bits of information and piece together a profile about the mobile user’s beliefs, preferences, convictions, and behaviour. Of course, it is unlikely that it would be the same commercial organisation detecting users in various contexts, but commercial users tend to sell-on their data for a profit, and it is then that data may not be fully anonymised or normalised before it is analysed.